Evidence action

Data Analysis Coordinator Job at evidence action

JOB VACANCY AT EVIDENCE ACTION

Position: Data Analysis Coordinator, MMS & SQ-LNS (Consultant)

Organization: Evidence Action

Locations: Abuja, Oyo State and Jigawa State, Nigeria

Work Arrangement: On-site

Team: Monitoring, Learning and Evaluation (MLE)

Contract Type: Consultant

Application Deadline: Not Specified

ABOUT EVIDENCE ACTION

Evidence Action is a global nonprofit organization that scales evidence-based, cost-effective interventions to improve lives. In Nigeria, the organization supports maternal and child nutrition programmes, including Multiple Micronutrient Supplementation (MMS) for pregnant women and Small-Quantity Lipid-based Nutrient Supplements (SQ-LNS) for children aged 6 to 18 months.

JOB SUMMARY

Evidence Action is seeking an experienced Data Analysis Coordinator to lead monitoring data management, analysis and reporting for its MMS and SQ-LNS programmes.

The successful candidate will manage the end-to-end monitoring data pipeline, from digital data collection and quality checks to analysis, dashboards and reports that help programme teams and government partners improve implementation and health outcomes.

KEY RESPONSIBILITIES

  1. Clean, manage and analyse monitoring data from health facilities and programme activities across supported states.
  2. Calculate key indicators relating to antenatal care, supplementation coverage, product availability, stockouts, child eligibility and programme performance.
  3. Produce cleaned datasets, topline summaries, performance reports, dashboards and priority-facility lists.
  4. Build and update SurveyCTO digital data collection forms and conduct high-frequency data quality checks.
  5. Develop reproducible, documented data cleaning and analysis workflows using Stata or R.
  6. Create visualizations, maps and presentations that communicate findings to programme teams and government stakeholders.
  7. Support household surveys, including data quality checks for anthropometric measurements and survey analysis.
  8. Identify data quality problems, recommend corrective actions and provide timely feedback to field teams.
  9. Prepare data packs for state and LGA review meetings and contribute to donor reports and learning products.
  10. Maintain secure data storage, documentation and compliance with data protection protocols.

QUALIFICATIONS AND REQUIREMENTS

Education

  • Bachelor’s degree in Statistics, Economics, Data Science, Public Health, Epidemiology or another quantitative field.
  • A Master’s degree in a quantitative discipline is an added advantage.

Experience

  • At least 5 years of experience in data management and analysis for health or nutrition programmes.
  • Experience in routine monitoring of facility-based or community-based programmes in Nigeria.
  • Proven ability to deliver accurate analysis under tight deadlines while managing multiple data streams.

Technical Skills

  • Strong proficiency in Stata and/or R.
  • Experience with SurveyCTO, ODK or KoBo for digital data collection.
  • Knowledge of descriptive and basic inferential statistics.
  • Advanced Microsoft Excel skills.
  • Experience with Power BI, R Shiny, Tableau or similar dashboard tools.
  • Ability to use AI tools and automation to improve data processing and reporting.
  • Familiarity with DHIS2, ANC and GMP registers, and logistics management information systems is desirable.
  • Experience in maternal and child nutrition data and mapping tools such as QGIS, ArcGIS or R is an advantage.

Additional Requirements

  • Strong analytical, communication and problem-solving skills.
  • Ability to explain technical findings to non-technical audiences.
  • Legal authorization to work in Nigeria.
  • Willingness to travel to programme states for up to 20% of working time, subject to security guidance.

HOW TO APPLY

Interested and qualified candidates should submit their applications through the official recruitment portal.

Primary Application Link

Alternative Application Link

Application Deadline: Not Specified

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